Machine Learning Based Video Compression

2. The system of claim 1, wherein the one or more local variables are based on the one or more features in the given frame, wherein the one or more global variables are based on one or more features common to a plurality of frames of the target content, and wherein the plurality of distributions indicate a likelihood of values for the one or more local variables and the one or more global variables.

4. The system of claim 3, wherein the global model comprises one or more of a long short-term memory model or a Kalman filter.

7. The system of claim 1, wherein the plurality of distributions corresponding to the latent space comprise noise, and are centered around means of the one or more global variables and the one or more local variables.

12. The computer-implemented method of claim 9, wherein the plurality of distributions corresponding to the latent space comprise noise, and are centered around means of the one or more global variables and the one or more local variables.

15. The computer-implemented method of claim 14, wherein the plurality of distributions indicate a likelihood of values for the one or more local variables and the one or more global variables.

19. The computer-implemented method of claim 14, wherein the plurality of distributions corresponding to the latent space are centered around means of the one or more global variables and the one or more local variables, and wherein random noise is added to the plurality of distributions.

20. The computer-implemented method of claim 14, wherein the latent space comprises a global density model corresponding to the one or more global variables and a local density model corresponding to the one or more local variables.